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Building Fuzzy Inference Systems with the Interval Type-2 Fuzzy Logic Toolbox., , , , , and . Analysis and Design of Intelligent Systems using Soft Computing Techniques, volume 41 of Advances in Soft Computing, Springer, (2007)Optimization of Deep Neural Network for Recognition with Human Iris Biometric Measure., , , and . NAFIPS, volume 648 of Advances in Intelligent Systems and Computing, page 172-180. Springer, (2017)Application of interval type-2 fuzzy neural networks in non-linear identification and time series prediction., , , and . Soft Comput., 18 (6): 1213-1224 (2014)Fuzzy granular gravitational clustering algorithm for multivariate data., , , and . Inf. Sci., (2014)Non-singleton Interval Type-2 Fuzzy Systems as Integration Methods in Modular Neural Networks Used Genetic Algorithms to Design., , and . Nature-Inspired Design of Hybrid Intelligent Systems, volume 667 of Studies in Computational Intelligence, Springer, (2017)Optimization of type-2 fuzzy weights in backpropagation learning for neural networks using GAs and PSO., , , , and . Appl. Soft Comput., (2016)Generalized Type-2 Fuzzy Systems for controlling a mobile robot and a performance comparison with Interval Type-2 and Type-1 Fuzzy Systems., , and . Expert Syst. Appl., 42 (14): 5904-5914 (2015)Big Five Patterns for Software Engineering Roles Using an ANFIS Learning Approach with RAMSET., , , and . MICAI (2), volume 6438 of Lecture Notes in Computer Science, page 428-439. Springer, (2010)Comparison between Choquet and Sugeno integrals as aggregation operators for pattern recognition., , , , , and . NAFIPS, page 1-6. IEEE, (2016)Fuzzy operators for quality evaluation in images edge detection., , , and . IFSA/NAFIPS, page 1182-1187. IEEE, (2013)